Traditional radar target recognition algorithms utilize balance data set to train the classifier
and achieve a satisfactory result on a balance test data set. However, in the case of non …

With the maturity of synthetic aperture radar (SAR) technology, the problem of imbalanced
data has gradually emerged. This problem makes it difficult for the automatic target …

Class imbalance is a prevalent problem in many real-world applications, and imbalanced
data distribution can dramatically skew the performance of classifiers. In general, the higher …

Synthetic aperture radar (SAR) images are difficult to acquire, and the number of images of
different targets often varies greatly, resulting in a large number of imbalanced class …

We present a modified local tangent space alignment (LTSA) algorithm, called supervised
kernel discriminant local tangent space alignment (SKDLTSA), for radar target recognition …

Automatic modulation recognition (AMR) of radar signals plays a critical role in electronic
reconnaissance. Current AMR algorithms are mainly based on convolutional neural …

A new manifold learning algorithm named adaptive neighborhood preserving discriminant
projection method is proposed for the feature extraction of high-range resolution profile …

High range resolution profile (HRRP) contains important target structure signatures and is
easy to be acquired, which makes HRRP target recognition keep drawing great attention …

A working pattern recognition model of airborne fire control radar for unbalanced data is
proposed in this paper, which consists of weight oversampling and proportional voting …

A kernel optimization method based on fusion kernel for high-resolution range profile
(HRRP) is proposed in this paper. Based on the fusion of-norm and-norm Gaussian kernels …